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84 lines
2 KiB
Text
84 lines
2 KiB
Text
[[query-dsl-text-expansion-query]]
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== Text expansion query
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++++
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<titleabbrev>Text expansion</titleabbrev>
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++++
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The text expansion query uses a {nlp} model to convert the query text into a
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list of token-weight pairs which are then used in a query against a
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<<sparse-vector,sparse vector>> or <<rank-features,rank features>> field.
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[discrete]
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[[text-expansion-query-ex-request]]
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=== Example request
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[source,console]
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----
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GET _search
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{
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"query":{
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"text_expansion":{
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"<sparse_vector_field>":{
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"model_id":"the model to produce the token weights",
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"model_text":"the query string"
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}
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}
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}
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}
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----
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// TEST[skip: TBD]
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[discrete]
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[[text-expansion-query-params]]
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=== Top level parameters for `text_expansion`
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`<sparse_vector_field>`:::
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(Required, object)
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The name of the field that contains the token-weight pairs the NLP model created
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based on the input text.
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[discrete]
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[[text-expansion-rank-feature-field-params]]
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=== Top level parameters for `<sparse_vector_field>`
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`model_id`::::
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(Required, string)
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The ID of the model to use to convert the query text into token-weight pairs. It
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must be the same model ID that was used to create the tokens from the input
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text.
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`model_text`::::
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(Required, string)
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The query text you want to use for search.
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[discrete]
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[[text-expansion-query-example]]
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=== Example
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The following is an example of the `text_expansion` query that references the
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ELSER model to perform semantic search. For a more detailed description of how
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to perform semantic search by using ELSER and the `text_expansion` query, refer
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to <<semantic-search-elser,this tutorial>>.
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[source,console]
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----
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GET my-index/_search
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{
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"query":{
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"text_expansion":{
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"ml.tokens":{
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"model_id":".elser_model_1",
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"model_text":"How is the weather in Jamaica?"
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}
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}
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}
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}
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----
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// TEST[skip: TBD]
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[NOTE]
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====
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Depending on your data, the text expansion query may be faster with `track_total_hits: false`.
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====
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